The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Window functions have been very popular in the user community and become an integral part of data warehouse queries. A class of window functions commonly used in data warehousing is reporting window functions. Database statements in data warehouse environments may involve multiple such reporting window functions at successive hierarchical levels.
Window functions such as reporting window functions are often used as foundational analysis tools for data sets. For example, one or more such window functions may be used to extract information on sales data stored in a database system. This information can be utilized by a company to track sales, evaluate policy, develop marketing strategy, project future growth, and perform various other tasks.
Records from one or more database tables can be grouped according to one or more partition-by keys. Reporting window functions can be calculated based on records in each group. The desired grouping can be specified in a database query, such as a SQL query.
Given the importance of window functions for data analysis, providing a quick result for database queries containing window functions is often an important database performance metric. To answer such a database query in an accelerated fashion, the database query can be formulated as parallel operations when creating a query execution plan for execution by database software in a hardware configuration.
Based on the foregoing, there is a need for developing techniques that can evaluate window functions in a highly efficient and scalable fashion.